An iterative linear algorithm for the analysis of oriented patterns
Fábio J. Ayres, Rangaraj Mandayam Rangayyan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Oriented patterns in an image often carry important information about the scene represented. Rao and Jain developed a technique to analyze images with oriented texture using phase portraits, where the parameters of a planar first-order phase portrait are locally estimated using a nonlinear least-squares algorithm. The method gives accurate results, but is computationally expensive. Shu and Jain proposed a faster linear method for the estimation of the parameters of the phase portrait. However, their formulation leads to the minimization of a different error measure, which is not as robust as the nonlinear least-squares procedure in the presence of noise, and also makes the implicit assumption that the orientation field was truly generated by a phase portrait model (with an extra weighting factor to compensate for noise sensitivity). We propose a new derivation of Shu and Jain's linear estimator that leads to similar estimation equations, while making explicit the nature of the error measure. Our procedure includes an iterative scheme, of which Shu and Jain's linear estimator is a particular case. We show that our estimator is more robust to noise than Shu and Jain's linear estimator.